Optimizing Parallel Task Execution for Multi-Agent Mission Planning

Multi-Agent Systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-robot missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. To overcome the aforementioned problem, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. To fill in the gap in the literature related to MT robot problem configurations, we provide a formalization of the mission planning problem, using MT robots, in the form of Integer Linear Programming and Constraint Programming (CP), to minimize the mission makespan. The models are validated in CPLEX and CP Optimizer on the set of benchmarks. Moreover, we provide a comprehensive performance analysis of both solvers, exploring their scalability and solution quality